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Top1 Introduction
Wireless Sensor Networks (WSN) have been more popular for processing information in risky environments such as health care monitoring systems, intruder surveillance, and environmental control systems. The sensor nodes execute the process of sensing, communication, and computing for the information. The nodes have limited computation and communication capabilities. Energy is an important factor to perform the communication and computation process. Many of the algorithms and methods were used to provide energy-efficient infrastructure.
The topology control technique is mainly used for providing the energy-efficient infrastructure, to consume the minimum energy for the communication that also improves the network lifetime. The main goal of topology control is to form the good connectivity of the network topology and to minimize the energy consumption. This method forms the virtual backbone that connects the set of active nodes for sending the information to the destination through intermediate nodes. The set of active nodes is used to reduce the routing path from source to destination. This method reduces the nodes to participation and forwards the routing information to the destination node.
The biologically inspired algorithms (Selvakennedy et al., 2007) are utilized to design the sensor networks. The complex behavior of the system is modified into the new environment and to find a solution by using simple rules. The insect or spices behavior is used to achieve the optimal solution to the problem. Some of the biologically inspired techniques are applied to obtain the solution for clustering and multi sink placement problem in wireless sensor networks.
Bacteria Foraging Algorithm (BFA) is one of the methods for the biologically inspired algorithm which is used to form the optimal nodes for connected dominating set in the topology construction. The performance of the BFA is evaluated with other algorithms such as A3, A1, GA, and GSA algorithms. The optimal topology generated by the BFA algorithm is used for extending the network lifetime and improving the connected sensing coverage area in the network.
The objectives of this paper can be summarized as follows:
- 1.
Topology control problem is formulated to prolong the network lifetime with the help of the number of the active nodes
- 2.
Bacteria Foraging Algorithm is utilized to find the optimal node position of connected dominating set in the topology construction.
- 3.
The proposed methodology is evaluated by using simulation studies. The simulation results achieve better results with the comparison of A3,A1,GA, and GSA algorithms by considering the performance metrics such as the number of active nodes, energy consumption, total connected sensing coverage area.
The motivation of the proposed work is presented as follows:
The energy-efficient structure is generated by using the topology control technique for consuming the minimum energy for the communication and also improving the network lifetime. The main goal of the topology control is to generate good connectivity of the active nodes for minimizing the energy consumption. By using the bacteria foraging algorithm produces the virtual backbone that connects the optimal set of active nodes for sending the information to the destination through intermediate nodes. The optimal set of active nodes is used to reduce the routing path from source to destination and also to increase the total connected sensing coverage area of the network.
The rest of the paper is organized as follows. Section 2 describes the methods and techniques used for the topology construction and biological inspired methods. Section 3 describes the bacteria foraging algorithm for finding the optimal node position that forms the connected dominating set for topology construction. Section 4 presents the performance evaluation of the bacteria foraging algorithm for generating the optimal topology construction. Section 5 provides the conclusion.
TopThe following papers for topology control algorithms in wireless sensor networks are summarized and shown in Table 1.